Method and system for updating an intent space and estimating intent based on an intent space

ABSTRACT

The present teaching relates to updating an intent space and estimating intent based on an intent space. In one example, an initial intent space is obtained. Each intent in the initial intent space is characterized in one or more dimensions. At least one model is received. Each of the at least one model provides features in each of the dimensions and relationship thereof. A new intent associated with an intent in the initial intent space is determined based on the at least one model. Based on the new intent, the initial intent space is updated to derive an updated intent space.

BACKGROUND

1. Technical Field

The present teaching generally relates to organizing, retrieving,presenting, and utilizing information. Specifically, the presentteaching relates to methods and systems for updating an intent space andestimating intent.

2. Discussion of Technical Background

The Internet has made it possible for a person to electronically accessvirtually any content at any time and from any location. The Internettechnology facilitates information publishing, information sharing, anddata exchange in various spaces and among different persons. One problemassociated with the rapid growth of the Internet is the so-called“information explosion,” which is the rapid increase in the amount ofavailable information and the effects of this abundance. As the amountof available information grows, the problem of managing the informationbecomes more difficult, which can lead to information overload. With theexplosion of information, it has become more and more important toprovide users with information from a public space that is relevant tothe individual person and not just information in general.

In addition to the public space such as the Internet, semi-privatespaces including social media and data sharing sites have become anotherimportant source where people can obtain and share information in theirdaily lives. The continuous and rapid growth of social media and datasharing sites in the past decade has significantly impacted thelifestyles of many; people spend more and more time on chatting andsharing information with their social connections in the semi-privatespaces or use such semi-private sources as additional means forobtaining information and entertainment. Similar to what has happened inthe public space, information explosion has also become an issue in thesocial media space, especially in managing and retrieving information inan efficient and organized manner.

Private space is another data source used frequently in people'severyday lives. For example, personal emails in Yahoo! mail, Gmail,Outlook etc. and personal calendar events are considered as privatesources because they are only accessible to a person when she or he logsin using private credentials. Although most information in a person'sprivate space may be relevant to the person, it is organized in asegregated manner. For example, a person's emails may be organized bydifferent email accounts and stored locally in different emailapplications or remotely at different email servers. As such, to get afull picture of some situation related to, e.g., some event, a personoften has to search different private spaces to piece everythingtogether. For example, to check with a friend of the actual arrival timefor a dinner, one may have to first check a particular email (in theemail space) from the friend indicating the time the friend will arrive,and then go to Contacts (a different private space) to search for thefriend's contact information before making a call to the friend toconfirm the actual arrival time. This is not convenient.

The segregation of information occurs not only in the private space, butalso in the semi-private and public spaces. This has led to anotherconsequential problem given the information explosion: requiring one toconstantly look for information across different segregated spaces topiece everything together due to lack of meaningful connections amongpieces of information that are related in actuality yet isolated indifferent segregated spaces.

Efforts have been made to organize the huge amount of availableinformation to assist a person to find the relevant information.Conventional scheme of such effort is application-centric and/ordomain-centric. Each application carves out its own subset ofinformation in a manner that is specific to the application and/orspecific to a vertical or domain. For example, such attempt is eitherdedicated to a particular email account (e.g., www.Gmail.com) orspecific to an email vertical (e.g., Outlook); a traditional web topicalportal allows users to access information in a specific vertical, suchas www.IMDB.com in the movies domain and www.ESPN.com in the sportsdomain. In practice, however, a person often has to go back and forthbetween different applications, sometimes across different spaces, inorder to complete a task because of the segregated and unorganizednature of information existing in various spaces. Moreover, even withina specific vertical, the enormous amount of information makes it tediousand time consuming to find the desired information.

Another line of effort is directed to organizing and providinginformation in an interest-centric manner. For example, user groups ofsocial media in a semi-private space may be formed by common interestsamong the group members so that they can share information that islikely to be of interest to each other. Web portals in the public spacestart to build user profiles for individuals and recommend content basedon an individual person's interests, either declared or inferred. Theeffectiveness of interest-centric information organization andrecommendation is highly relied on the accuracy of user profiling.Oftentimes, however, a person may not like to declare her/his interests,whether in a semi-private space or a public space. In that case, theaccuracy of user profiling can only be relied on estimation, which canbe questionable. Accordingly, neither of the application-centric,domain-centric, and interest-centric ways works well in dealing with theinformation explosion challenge.

FIG. 1 depicts a traditional scheme of information organization andretrieval in different spaces in a segregated and disorganized manner. Aperson 102 has to interact with information in private space 104,semi-private space 106, and public space 108 via unrelated and separatemeans 110, 112, 114, respectively. For accessing private data from theprivate space 104, means 110, such as email applications, email sites,local or remote Contacts and calendars, etc., has to be selected andused. Each means 110 is domain or application-oriented, allowing theperson 102 to access information related to the domain with the specificapplication that the means 110 is developed for. Even for informationresiding within different applications/domains in the private space 104,a person 102 still has to go by different means 110 to access content ofeach application/domain, which is not convenient and not person-centric.For example, in order to find out the phone numbers of attendees of abirthday party, the person 102 has to first find all the confirmationemails from the attendees (may be sent in different emails and even todifferent email accounts), write down each name, and open differentContacts to look for their phone numbers.

Similarly, for interacting with the semi-private space 106, a person 102needs to use a variety of means 112, each of which is developed anddedicated for a specific semi-private data source. For example, Facebookdesktop application, Facebook mobile app, and Facebook site are allmeans for accessing information in the person 102's Facebook account.But when the person 102 wants to open any document shared on Dropbox bya Facebook friend, the person 102 has to switch to another meansdedicated to Dropbox (a desktop application, a mobile app, or awebsite). As shown in FIG. 1, information may be transmitted between theprivate space 104 and the semi-private space 106. For instance, privatephotos can be uploaded to a social media site for sharing with friends;social media or data sharing sites may send private emails to a person102's private email account notifying her/him of status updates ofsocial friends. However, such information exchange does notautomatically create any linkage between data between the private andsemi-private spaces 104, 106. Thus, there is no application that cankeep track of such information exchange and establish meaningfulconnections, much less utilizing the connections to make it easier tosearch for information.

As to the public space 108, means 114 such as traditional search engines(e.g., www.Google.com) or web portals (e.g., www.CNN.com, www.AOL.com,www.IMDB.com, etc.) are used to access information. With the increasingchallenge of information explosion, various efforts have been made toassist a person 102 to efficiently access relevant and on-the-pointcontent from the public space 108. For example, topical portals havebeen developed that are more domain-oriented as compared to genericcontent gathering systems such as traditional search engines. Examplesinclude topical portals on finance, sports, news, weather, shopping,music, art, movies, etc. Such topical portals allow the person 102 toaccess information related to subject matters that these portals aredirected to. Vertical search has also been implemented by major searchengines to help to limit the search results within a specific domain,such as images, news, or local results. However, even if limiting thesearch result to a specific domain in the public space 108, there isstill an enormous amount of available information, putting much burdenon the person 102 to identify desired information.

There is also information flow among the public space 108, thesemi-private space 106, and the private space 104. For example,www.FedeEx.com (public space) may send a private email to a person 102'semail account (private space) with a tracking number; a person 102 mayinclude URLs of public websites in her/his tweets to followers. However,in reality, it is easy to lose track of related information residing indifferent spaces. When needed, much effort is needed to dig them outbased on memory via separate means 110, 112, 114 across different spaces104, 106, 108. In today's society, this consumes more and more people'stime.

Because information residing in different spaces or even within the samespace is organized in a segregated manner and can only be accessed viadedicated means, the identification and presentation of information fromdifferent sources (whether from the same or different spaces) cannot bemade in a coherent and unified manner. For example, when a person 102searches for information using a query in different spaces, the resultsyielded in different search spaces are different. For instance, searchresult from a conventional search engine directed to the public space108 is usually a search result page with “blue links,” while a search inthe email space based on the same query will certainly look completelydifferent. When the same query is used for search in different socialmedia applications in the semi-private space 106, each application willagain likely organize and present the search result in a distinctmanner. Such inconsistency affects user experience. Further, relatedinformation residing in different sources is retrieved piece meal sothat it requires the person 102 to manually connect the dots provide amental picture of the overall situation.

Therefore, there is a need for improvements over the conventionalapproaches to organize, retrieve, present, and utilize information.

SUMMARY

The present teaching relates to methods, systems, and programming forupdating an intent space and estimating intent based on an intent space.

In one example, a method, implemented on at least one computing deviceeach having at least one processor, storage, and a communicationplatform connected to a network for updating an intent space ispresented. An initial intent space is obtained. Each intent in theinitial intent space is characterized in one or more dimensions. Atleast one model is received. Each of the at least one model providesfeatures in each of the dimensions and relationship thereof. A newintent associated with an intent in the initial intent space isdetermined based on the at least one model. Based on the new intent, theinitial intent space is updated to derive an updated intent space.

In another example, a method, implemented on at least one computingdevice each having at least one processor, storage, and a communicationplatform connected to a network for estimating intent based on an intentspace having a plurality of dimensions is presented. An input isreceived from a person. The input is analyzed to determine one or morefeatures related to at least one dimension of the intent space. Anintent in the intent space is determined based on the one or morefeatures.

Other concepts relate to software for implementing the present teachingon updating an intent space and estimating intent based on an intentspace. A software product, in accord with this concept, includes atleast one non-transitory machine-readable medium and information carriedby the medium. The information carried by the medium may be executableprogram code data, parameters in association with the executable programcode, and/or information related to a user, a request, content, orinformation related to a social group, etc.

In one example, a non-transitory, machine-readable medium havinginformation recorded thereon for updating an intent space is presented.The recorded information, when read by the machine, causes the machineto perform a series of processes. An initial intent space is obtained.Each intent in the initial intent space is characterized in one or moredimensions. At least one model is received. Each of the at least onemodel provides features in each of the dimensions and relationshipthereof. A new intent associated with an intent in the initial intentspace is determined based on the at least one model. Based on the newintent, the initial intent space is updated to derive an updated intentspace.

In another example, a non-transitory machine readable medium havinginformation recorded thereon for estimating intent based on an intentspace having a dimension is presented. The recorded information, whenread by the machine, causes the machine to perform a series ofprocesses. An input is received from a person. The input is analyzed todetermine one or more features related to at least one dimension of theintent space. An intent in the intent space is determined based on theone or more features.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems, and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 (prior art) depicts a traditional scheme of informationorganization and retrieval from different spaces in a segregated anddisorganized manner;

FIG. 2 depicts a novel scheme of building a person-centric space for aperson by cross-linking data from different spaces and applicationsthereof, according to an embodiment of the present teaching;

FIG. 3 illustrates exemplary types of data sources in a private space;

FIG. 4 illustrates exemplary types of data sources in a semi-privatespace;

FIG. 5 depicts an exemplary system diagram of a person-centric INDEXsystem, according to an embodiment of the present teaching;

FIG. 6 is a flowchart of an exemplary process for building aperson-centric space, according to an embodiment of the presentteaching;

FIG. 7 is a flowchart of an exemplary process for applying aperson-centric space for digital personal assistance, according to anembodiment of the present teaching;

FIG. 8 depicts an exemplary scheme of building a person-centric spacefor each individual person via a person-centric INDEX system andapplications thereof, according to an embodiment of the presentteaching;

FIG. 9 depicts an exemplary scheme in which a variety of dynamic cardsare built and provided to a person based on different intents estimatedfor the same query in different contexts, according to an embodiment ofthe present teaching;

FIG. 10 illustrates an exemplary answer card, according to an embodimentof the present teaching;

FIG. 11 illustrates an exemplary search results card, according to anembodiment of the present teaching;

FIG. 12 depicts an exemplary scheme of automatic online order emailsummary and package tracking via cross-linked data in a person-centricspace, according to an embodiment of the present teaching;

FIG. 13 illustrates an exemplary task with a list of task actions forautomatic package tracking;

FIG. 14 illustrates a series of exemplary cards provided to a person inthe process of automatic online order email summary and packagetracking;

FIG. 15 illustrates exemplary entities extracted from a person-centricspace and their relationships established in the process of automaticonline order email summary and package tracking;

FIG. 16 illustrates an exemplary intent space, according to anembodiment of the present teaching;

FIG. 17 depicts an exemplary scheme of combining personal intent spacesinto a common intent space, according to an embodiment of the presentteaching;

FIG. 18 depicts an exemplary system diagram of an intent engineincluding an intent estimator and an intent database builder, accordingto an embodiment of the present teaching;

FIG. 19 is a flowchart of an exemplary process for an intent estimator,according to an embodiment of the present teaching;

FIG. 20 is a flowchart of another exemplary process for an intentestimator, according to an embodiment of the present teaching;

FIG. 21 is a flowchart of an exemplary process for an intent databasebuilder, according to an embodiment of the present teaching;

FIG. 22 is a flowchart of another exemplary process for an intentdatabase builder, according to an embodiment of the present teaching;

FIG. 23 illustrates an exemplary domain taxonomy, according to anembodiment of the present teaching;

FIG. 24 illustrates an exemplary action graph, according to anembodiment of the present teaching;

FIG. 25 depicts an exemplary scheme of teaching new intent by experts,according to an embodiment of the present teaching;

FIG. 26 depicts the architecture of a mobile device which can be used toimplement a specialized system incorporating the present teaching; and

FIG. 27 depicts the architecture of a computer which can be used toimplement a specialized system incorporating the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present teaching describes methods, systems, and programming aspectsof efficiently and effectively organizing, retrieving, presenting, andutilizing information.

FIG. 2 depicts a novel scheme of building a person-centric space 200 fora person 102 by cross-linking data from different spaces andapplications thereof, according to an embodiment of the presentteaching. Unlike the traditional approach to organize information indifferent spaces in a segregated and disorganized manner, as illustratedin FIG. 1, FIG. 2 provides a person-centric INDEX system 202, whichbuilds the person-centric space 200 specific to the person 102 bydigesting information from the public space 108, semi-private space 106,and private space 104 and cross-linking relevant data from those spaces104, 106, 108. As described herein, a person 102 referred herein mayinclude a human being, a group of people, an organization such as abusiness department or a corporation, or any unit that can use theperson-centric INDEX system 202. A space, whether private, semi-private,or public, may be a collection of information in one or more sources.Through the person-centric INDEX system 202, information relevant to theperson 102 from each of the private, semi-private, and public spaces104, 106, and 108 is projected, into the person-centric space 200 in ameaningful manner. That is, a part of the data in the person-centricspace 200 is projected from the public space 108 in a manner relevant tothe person 102; a part of the data in the person-centric space 200 isprojected from the semi-private space 106 in a manner relevant to theperson 102; a part of the data in the person-centric space 200 isprojected from the private space 104. Thus, the person-centric space 200is an information universe meaningful to the person 102 and formed fromthe perspective of the person 102.

Different from conventional approaches, which organize information in anapplication-centric, domain-centric, or interest-centric manner, theperson-centric INDEX system 202 recognizes relevant information from theenormous information available in the public space 108, semi-privatespace 106, and private space 104 in accordance with the perspective ofthe person 102, thereby filtering out information that is not relevantto the person 102, assisting the person 102 to make sense out of therelevance among different pieces of information in the person-centricspace. The person-centric space 200 is dynamic and changes with theonline (possibly offline) activities of the person 102. For example, theperson 102 can search more content via the person-centric INDEX system202 (this function may be similar to conventional search engine) thatwill lead to the continuously expansion of the person-centric space 200.The person-centric INDEX system 202 can cross-link data acrossinformation different spaces, or information from different sources inthe same space. For instance, by identifying a FedEx tracking number inan order confirmation email sent to a personal email account fromwww.Amazon.com, the person-centric INDEX system 202 can automaticallysearch for any information in any space that is relevant to the trackingnumber, such as package delivery status information from www.FedEx.comin the public space 108. Although most information from www.FedEx.commay not be related to the person 102, the particular package deliverystatus information relevant to the person 102 and can be retrieved bythe person-centric INDEX system 202 and indexed against the informationfrom the person 102's private emails. In other words, the packagedelivery status information, even though from the public space 108, canbe projected into the person-centric space 200 and, together with otherinformation in the person-centric space 200 (such as a confirmationemail related to the package), the person-centric INDEX system 202integrates relevant information from different sources to yield unifiedand semantically meaningful information, such as a card related to anorder incorporating the name of the ordered item, the name of the personwho ordered it, the name of the company that is to deliver the item, aswell as the current delivery status.

In another example, when a private email reminding of an upcoming soccergame from a coach is received, the person-centric INDEX system 202 maybe triggered to process the private email and identify, based on thecontent of the email, certain information in the sports domain such asdate/time, location, and players and coaches of the soccer game andcross link the email with such information. The person-centric INDEXsystem 202 may also retrieve additional relevant information from otherdata sources, such as phone number of the coach from Contacts of theperson 102. The person-centric INDEX system 202 may also retrieve mapand directions to the soccer game stadium from Google Maps based on thelocation information and retrieve weather forecast of the game fromwww.Weather.com based on the date. If the coach is connected with theperson 102 in any social media, then the person-centric INDEX system 202may go to the social media site in the semi-private space 106 toretrieve any content made by the coach that is relevant to the soccergame. In this example, all those different pieces of information fromthe public space 108, semi-private space 106, and private space 104 arecross-linked and projected to the person-centric space 200 in accordancewith the person 102's perspective on the soccer game.

The person-centric INDEX system 202 may build the initial person-centricspace 200 when the person 102 first time accesses the person-centricINDEX system 202. By analyzing all the information in the private space104 which the person 102 has granted access permission, theperson-centric INDEX system 202 can identify, retrieve, and linkrelevant information from the public space 108, semi-private space 106,and private space 104 and project them into the person-centric space200. As mentioned above, the person-centric INDEX system 202 alsomaintains and updates the person-centric space 200 in a continuous ordynamic manner. In one example, the person-centric INDEX system 202 mayautomatically check any change, either in the private space 104 orotherwise, based on a schedule and initiates the update of theperson-centric space 200 when necessary. For example, every two hours,the person-centric INDEX system 202 may automatically check any newemail that has not been analyzed before. In another example, theperson-centric INDEX system 202 may automatically check any changeoccurring in the public space 108 and the semi-private space 106 that isrelevant to the person 102. For instance, in the soccer game exampledescried above, every day before the scheduled soccer game, theperson-centric INDEX system 202 may automatically check www.Weather.comto see if the weather forecast needs to be updated. The person-centricINDEX system 202 may also update the person-centric space 200 responsiveto some triggering event that may affect any data in the person-centricspace 200. For example, in the FedEx package example described above,once the scheduled delivery date has passed or a package delivery emailhas been received, the person-centric INDEX system 202 may update theperson-centric space 200 to remove the temporary relationship betweenthe person 102 and www.FedEx.com until a new connection between them isestablished again in the future. The triggering event is not limited toevents happening in the public space 108, semi-private space 106, orprivate space 104, but can include any internal operation of theperson-centric INDEX system 202. As an example, every time theperson-centric INDEX system 202 performs a search in response to a queryor to answer a question, it may also trigger the person-centric INDEXsystem 202 to update the person-centric space 200 based on, e.g., newlyretrieved information related to, e.g., a search result or some answers.When the search result or answers cannot be found in the person-centricspace 200, the person-centric INDEX system 202 may also update theperson-centric space 200 to include those search results and answers.That is, the person-centric INDEX system 202 may dynamically update theperson-centric space 200 in response to any suitable triggering events.

To better understand information in the person-centric space 200 andmake it meaningful, the person-centric INDEX system 202 may furtherbuild a person-centric knowledge database including person-centricknowledge by extracting and associating data about the person 102 fromthe person-centric space 200. The person-centric INDEX system 202 canextract entities related to the person 102 and infer relationshipsbetween the entities without the person 102's explicit declaration. Aperson-centric knowledge representation for the person 102 can becreated by person-centric INDEX system 202 the based on the entities andrelationships. The inference can be based on any information in theperson-centric space 200. The knowledge elements that can be inferred ordeduced may include the person 102's social contacts, the person 102'srelationships with places, events, etc.

In order to construct the person-centric knowledge representation, theperson-centric INDEX system 202 may extract entities from content in theperson 102's person-centric space 200. These entities can be places likerestaurants or places of interest, contact mentions like names, emails,phone numbers or addresses, and events with date, place and personsinvolved. In addition to extracting these mentions, the person-centricINDEX system 202 can resolve them to what they refer to (i.e. candisambiguate an extracted entity when it may refer to multipleindividuals). For example, a word “King” in a private email may refer toa title of a person who is the King of a country or refer to a person'slast name. The person-centric INDEX system 202 may utilize anyinformation in the person-centric space 200 to determine what type ofentity the word “King” refers to in the email. In addition todetermining an entity type for an extracted entity name, theperson-centric INDEX system 202 may also determine a specific individualreferred to by this entity name. As one instance, a person's first namemay refer to different Contacts, and a same restaurant name can refer toseveral restaurants. The person-centric INDEX system 202 can make use ofcontextual information and/or textual metadata associated with theentity name in the email to disambiguate such cases, thereby providing ahigh precision resolution. With the precise disambiguation, theperson-centric INDEX system 202 can find right information fromunstructured personal data and provide it in a structured way (e.g. in agraph associated with the person 102). In contrast to a conventionalpersonal profile, the person-centric INDEX system 202 generates a singlepersonal graph for an individual to encompass connections, interests,and events associated with the person 102. It can be understood that aperson-centric knowledge may also be represented in a format other thana graph.

The person-centric INDEX system 202, in conjunction with theperson-centric space 200, may organize related information fromdifferent sources and provide the information to a person 102 in auser-friendly, unified presentation style. In addition to providingrequested information in any known format, such as hyperlinks on asearch results page, the person-centric INDEX system 202 may presentinformation in intent-based cards. Unlike existing entity-based searchresults cards organizing results based on an entity, the person-centricINDEX system 202 may focus on a person 102's intent to dynamically builda card for the person 102. The intent may be explicitly specified in thequery, or estimated based on the context, trending events, or anyknowledge derived from the person-centric space 200. Knowing the person102's intent when the card is created to answer the query, theperson-centric INDEX system 202 can provide relevant information on thecard. The relevant information may include partial informationassociated with the entity in the query, and/or additional informationfrom the person-centric space 200 that is related to the person'sintent. In the soccer game example descried above, in response to theperson's query or question related to the soccer game, theperson-centric INDEX system 202 may estimate the person's intent is toknow the date/time of the game and thus, build a card that includes notonly the direct answer of the date/time but also other informationrelated to the soccer game in the person-centric space 200, such as themap and directions, weather forecast, and contact information of thecoach.

In one embodiment, knowing the current intent of the person 102, theperson-centric INDEX system 202 can anticipate the next intent of theperson 102, such that the current card provided by the person-centricINDEX system 202 can lead to next steps. For example, the person-centricINDEX system 202 can anticipate that after looking at the show times ofa new movie, the person 102 will be likely to buy tickets. In anotherembodiment, focusing on the person 102's intent, the person-centricINDEX system 202 can answer the person 102 with a card even when thereis no entity in the query or request (i.e., in a query-less oranticipatory use case). For example, if the person-centric INDEX system202 determines that the person 102 has a behavior pattern of searchingtraffic information from work place to home at 5 pm on workdays, thenfrom now on, the person-centric INDEX system 202 may automaticallygenerate and provide a notice card to the person 102 at around 5 pm onevery workday, to notify the person 102 about the traffic informationregardless whether a query is received from the person 102.

The person-centric INDEX system 202 can be used for both building theperson-centric space 200 for a person 102 and facilitating the person102 to apply the person-centric space 200 in a variety for applications.Instead of using different means 110, 112, 114 shown in FIG. 1 to accessdifferent data sources across different spaces, the person-centric INDEXsystem 202 can serve as a centralized interface between the person 102and her/his own person-centric space 200, thereby reducing the time andefforts spent by the person 102 on retrieving desired information or anyother applications. As different pieces of relevant information from thepublic space 108, semi-private space 106, and private space 104 havebeen projected to the person-centric space 200 in a well-organized way,they can be handled by a single person-centric INDEX system 202, therebyimproving the efficiency and effectiveness in finding the desiredinformation. For example, in the FedEx package example described above,any time the person wants to know the current status of the package,she/he no longer needs to dig out the email with the tracking number,write down the tracking number, and open www.FedEx.com in a browser andtype in the tracking number. The person-centric INDEX system 202 mayhave already stored the package delivery status information since thetime when the initial order email was received and have kept updatingthe package delivery status information in the person-centric space 200.So any time when the person 102 inputs a request for package deliverystatus update, either in the form of a search query or a question n, theperson-centric INDEX system 202 can go directly to retrieve the updatedpackage delivery status information from the person-centric space 200 orautomatically call the tracking application programming interface (API)of FedEx server with the stored tracking number for the current statusupdate. The result is then provided to the person 102 without anyadditional efforts made by the person 102. In some embodiments, theperson 102 may not even need to explicitly request the status update.Responsive to receiving the order confirmation email, the person-centricINDEX system 202 may automatically set up a task to regularly send thestatus update to the person 102 until the package is delivered or maydynamically notify the person 102 with any event, like if the package isdelayed or lost.

In one aspect of the present teaching, the person-centric INDEX system202, in conjunction with the person-centric space 200, can be used foranswering questions. To achieve this, the person-centric INDEX system202 may classify a question from a person 102 into a personal questionor a non-personal question. In some embodiment, data from theperson-centric space 200 may be for classification. For example, aquestion related to “uncle Sam” may be classified as a personal questionif “uncle Sam” is a real person identified from the private Contacts.Once the question is classified as personal, the person-centric INDEXsystem 202 may extract various features including entities andrelationships from the question. The extracted entities andrelationships may be used by the person-centric INDEX system 202 totraverse a person-centric knowledge database derived from theperson-centric space 200. In some embodiments, the person-centricknowledge database may store data in a triple format including one ormore entities and relationships between the one or more entities. Whenan exact match of relationship and entity are found, an answer isreturned. When there is no exact match, a similarity between thequestion and answer triples is taken into consideration and used to findthe candidate answers. In the “uncle Sam” example described above, ifthe question is “where is uncle Sam,” the person-centric INDEX system202 may search the person-centric knowledge database for any locationentity that has a valid relationship with the entity “uncle Sam.” In oneexample, a recent email may be sent by “uncle Sam,” and the email mayalso mention that he will be attending a conference on these days. Thelocation of the conference can be retrieved from the conference websitein the public space 108, stored in the person-centric space 200, andassociated with entity “uncle Sam.” Based on the relationship, theperson-centric INDEX system 202 can answer the question with thelocation of the conference. The person-centric INDEX system 202 thusprovides an efficient solution to search for answers to personalquestions and increases user engagement and content understanding.

In another aspect of the present teaching, the person-centric INDEXsystem 202, in conjunction with the person-centric space 200, can beused for task completion. Task completion often involves interactionswith different data sources across different spaces. A task such as“making mother's day dinner reservation” involves task actions such asidentifying who is my mother, checking what date is mother's day thisyear, finding out a mutually available time slot on mother's day for mymother and me, picking up a restaurant that my mother and I like, makingan online reservation on the restaurant's website, etc. Traditionally,in order to complete each task action, a person 102 has to open a numberof applications to access information from different sources acrossdifferent spaces and perform a series of tedious operations, such assearching for “mother's day 2015” in a search engine, checking my owncalendar and mother's shared calendar, digging out past emails about therestaurant reservations for dinners with my mother, making onlinereservation via a browser, etc. In contrast to the traditionalapproaches for task completion, the person-centric INDEX system 202 cancomplete the same task more efficiently and effectively because allpieces of information related to mother's day dinner reservation havealready been projected to the person-centric space 200. This makesautomatic task generation and completion using the person-centric INDEXsystem 202 possible. In response to receiving an input of “makingmother's day dinner reservation” from a person 102, the person-centricINDEX system 202 can automatically generate the list of task actions asmentioned above and execute each of them based on information from theperson-centric space 200 and update the person 102 with the currentstatus of completing the task.

With the dynamic and rich information related to the person 102 that isavailable in the person-centric space 200, the person-centric INDEXsystem 202 can even automatically generate a task without any input fromthe person 102. In one embodiment, anytime a card is generated andprovided to the person 102, the information on the card may be analyzedby the person-centric INDEX system 202 to determine whether a task needsto be generated as a follow-up of the card. For example, once an emailcard summarizing an online order is constructed, the person-centricINDEX system 202 may generate a task to track the package deliverystatus until it is delivered and notify any status update for the person102. In another embodiment, any event occurring in the public space 108,semi-private space 106, or private space 104 that is relevant to theperson 102 may trigger the task completion as well. For instance, aflight delay message on an airline website in the public space 108 maytrigger generation of a task for changing hotel, rental car, andrestaurant reservations in the same trip. In still another embodiment,the person 102's past behavior patterns may help the person-centricINDEX system 202 to anticipate her/his intent in the similar context andautomatically generate a task accordingly. As an instance, if the person102 always had a dinner with her/his mother on mother's day at the samerestaurant, a task may be generated by the person-centric INDEX system202 this year, in advance, to make the mother's day dinner reservationat the same restaurant.

It is understood that in some occasions, certain task actions may not becompleted solely based on information from the person-centric space 200.For example, in order to complete the task “sending flowers to mom onmother's day,” flower shops need to be reached out to. In one embodimentof the present teaching, a task exchange platform may be created tofacilitate the completion of tasks. The person-centric INDEX system 202may send certain tasks or task actions to the task exchange platform sothat parties interested in completing the task may make bids on it. Thetask exchange platform alone, or in conjunction with the person-centricINDEX system 202, may select the winning bid and update the person 102with the current status of task completion. Monetization of taskcompletion may be achieved by charging service fee to the winning partyand/or the person 102 who requests the task.

In still another aspect of the present teaching, the person-centricINDEX system 202, in conjunction with the person-centric space 200, canbe used for query suggestions. By processing and analyzing data from theperson-centric space 200, the person-centric INDEX system 202 may builda user corpus database, which provides suggestions based on informationfrom the private space 104 and/or semi-private space 106. In response toany input from a person 102, the person-centric INDEX system 202 mayprocess the input and provide suggestions to the person 102 at runtimebased on the person 102's relevant private and/or semi-private data fromthe user corpus database as well other general log-based querysuggestion database and search history database. The query suggestionsmay be provided to the person 102 with very low latency (e.g., less than10 ms) in response to the person 102's initial input. Further, in someembodiments, before presenting to the person 102, suggestions generatedusing the person 102's private and/or semi-private data from the usercorpus database may be blended with suggestions produced based ongeneral log-based query suggestion database and search history database.Such blended suggestions may be filtered and ranked based on variousfactors, such as type of content suggested (e.g., email, social mediainformation, etc.), estimated intent based on an immediate previousinput from the person 102, context (e.g., location, data/time, etc.)related to the person 102, and/or other factors.

FIG. 3 illustrates exemplary types of data sources in a private space.The private space of a person may include any data source that isprivate to the person. For example, the private space may include anydata source that requires access information of the person (e.g.,password, token, biometric information, or any user credentials). Theprivate space may also include any data source that is intended to beaccessed only by the person even without requiring access control, suchas data on a person's smart phone that does not require password orfinger print verification. In this illustration, the private spaceincludes several categories of data sources such as emails, Contacts,calendars, instant messaging, photos, usage records, bookmarks, etc.Emails include emails stored in remote email servers such as Yahoo!Mail, Gmail, Hotmail, etc. and local emails in an email application on apersonal computer or mobile device. Instant messaging includes anymessages communicated between the person 102 and others via any instantmessaging applications, for example, Yahoo! Messenger, WhatsApp,Snapchat, to name a few. Usage records may be any logs private to theperson, such as, but not limited to, browsing history and call records.It is understood that the examples described above are for illustrativepurpose and are not intended to be limiting.

FIG. 4 illustrates exemplary types of data sources in a semi-privatespace. The semi-private space of a person may include any data sourcethat is accessible for a group of people designated by the person. Oneexample of data sources in the semi-private space is social media, suchas Tumblr, Facebook, Twitter, LinkedIn, etc. A person can designate agroup of people who can access her/his information shared in the socialmedia sites, such as status updates, posts, photos, and comments.Another example of data sources in the semi-private space is a contentsharing site. For instance, a person can share photos with family andfriends at Flickr, share work documents with colleagues or classmates atGoogle Docs, and share any files at Dropbox. It is understood that insome cases, there is not a clear boundary between a data source in theprivate space and a data source in the semi-private space. For instance,if a person restricts photos at Flickr to be only accessible byher/himself, then Flickr becomes a private source of the person, justlike local photos stored on the person's device. Similarly, when theentire or a portion of a calendar is shared with others, the calendarbecomes part of the semi-private space. It is understood that theexamples described above are for illustrative purpose and are notintended to be limiting.

FIG. 5 depicts an exemplary system diagram of the person-centric INDEXsystem 202, according to an embodiment of the present teaching. Theperson-centric INDEX system 202 includes a user interface 502 thatconnects a person 102 with multiple front-end components including asuggestion engine 504, a query interface 506, a Q/A interface 508, atask interface 510, and a contextual information identifier 512 coupledwith a user database 514. To support the front-end components, theperson-centric INDEX system 202 further includes multiple functionalcomponents including a search engine 516, a Q/A engine 518, a taskgeneration engine 520, a task completion engine 522, an intent engine524, a person-centric knowledge retriever 526, and a dynamic cardbuilder 528. In the back-end, the person-centric INDEX system 202includes a variety of databases for storing information in differentforms for different purposes, such as the person-centric space 200having a public database 544, a semi-private database 546, and a privatedatabase 548. The person-centric space 200 in this embodiment is builtup by a cross-linking engine 542. The person-centric INDEX system 202further includes a knowledge engine 530 for building a person-centricknowledge database 532 by processing and analyzing information in theperson-centric space 200. In addition, additional types of analyticresults from the knowledge engine 530 based on data from theperson-centric space 200 and/or any other suitable data sources may bestored in an intent database 534, a card module database 536, and a tasktemplate database 538.

A person 102 may interact with the person-centric INDEX system 202 viathe user interface 502 by providing an input. The input may be made by,for example, typing in a query, question, or task request, or clickingor touching any user interface element in the user interface 502 toenter a query, question, or task request. With each input from theperson 102, the suggestion engine 504 provides a list of suggestions tofacilitate the person 102 to complete the entire input. In thisembodiment, the suggestion engine 504 may provide suggestions based onthe person's private and/or semi-private information retrieved by theperson-centric knowledge retriever 526 from the person-centric space 200and/or the person-centric knowledge database 532. Those suggestionsinclude, for example, a contact name from the private Contacts, part ofa tweet from Twitter, or a package tracking status stored in theperson-centric space 200. In some embodiments, the suggestion engine 504may blend those suggestions based on the person 102's private and/orsemi-private information with the conventional suggestions based onpopular query logs and search history. In this embodiment, the intentengine 524 may provide an estimated intent associated with each input tohelp filtering and/or ranking the suggestions provided to the person102.

Each of the query interface 506, Q/A interface 508, and task interface510 is configured to receive a particular type of user inputs andforward them to the respective engine for handling. Once the results arereturned from the respective engine and/or from the dynamic card builder528, each of the query interface 506, Q/A interface 508, and taskinterface 510 forwards the results to the user interface 502 forpresentation. In one embodiment, the user interface 502 may firstdetermine the specific type of each input and then dispatch it to thecorresponding interface. For example, the user interface 502 mayidentify that an input is a question based on semantic analysis orkeyword matching (e.g., looking for keywords like “why” “when” “who,”etc. and/or a question mark). The identified question is then dispatchedto the Q/A interface 508. Similarly, the user interface 502 maydetermine, based on semantic analysis and/or machine learningalgorithms, that an input is a task request and forward the input to thetask interface 510. For any input that cannot be classified or does notfall within the categories of question and task request, the userinterface 502 may forward it to the query interface 506 for generalquery search. It is understood that, in some embodiments, the userinterface 502 may not classify an input first, but instead, forward thesame input to each of the query interface 506, Q/A interface 508, andtask interface 510 to have their respective engines to process the inputin parallel.

Another function of the user interface 502 involves presentinginformation to the person 102 either as responses to the inputs, such assearch results, answers, and task status, or as spontaneous notices,reminders, and updates in response to any triggering events. In thisembodiment, the information to be presented to the person 102 via theuser interface 502 may be presented in the form of cards that aredynamically built on-the-fly by the dynamic card builder 528 based onthe intent estimated by the intent engine 524. The cards may be ofdifferent types, such as an email card summarizing one or more relatedemails, a search results card summarizing information relevant to one ormore search results, an answer card including an answer to a questionwith additional information associated with the answer, or a notice cardthat is automatically generated to notify the person 102 of any event ofinterest. Based on its type, a card may be dispatched to one of thequery interface 506, Q/A interface 508, and task interface 510 andeventually presented to the person 102 via the user interface 502. Inaddition to cards, information in any other format or presentationstyles, such as search results in a research results page with “bluelinks” or answers in plain text, may be provided by the search engine516 and the Q/A engine 518 directly to the query interface 506 and Q/Ainterface 508, respectively. It is understood that the user interface502 may also provide information in a hybrid matter, meaning that someinformation may be presented as cards, while other information may bepresented in its native format or style.

As the user interface 502 receives an input from the person 102, it alsotriggers the contextual information identifier 512 to collect anycontextual information related to the person 102 and the input of theperson 102. The contextual information identifier 512 in this embodimentreceives user-related information from the user database 514, such asthe person 102's demographic information and declared and inferredinterests and preferences. Another source of contextual information isthe person 102's device including, for example, date/time obtained fromthe timer of the person 102's device, location obtained from a globalpositioning system (GPS) of the person 102's device, and informationrelated to the person 102's device itself (e.g., the device type, brand,and specification). Further, the contextual information identifier 512may also receive contextual information from the user interface 502,such as one or more inputs immediately before the current input (i.e.,user-session information). Various components in the person-centricINDEX system 202, including the cross-linking engine 542, knowledgeengine 530, and intent engine 524, may take advantage of the contextualinformation identified by the contextual information identifier 512.

The intent engine 524 in this embodiment has two major functions:creating and updating the intent database 534 and estimating an intentbased on the information stored in the intent database 534. The intentdatabase 534 may store a personal intent space which includes all theintents that make sense to the person 102 in the form of an action plusa domain. For example, based on the person 102's search history, theintent engine 524 may identify that the person 102 has repeatedlyentered different queries all related to the same intent “makingrestaurant reservations.” This intent then may be stored as a data pointin the person's personal intent space in the intent database 534 in theform of {action=making reservations; domain=restaurant}. More and moredata points will be filled into the personal intent space as the person102 continues interacting with the person-centric INDEX system 202. Insome embodiments, the intent engine 524 may also update the personalintent space in the intent database 534 by adding new intents based onexisting intents. For instance, the intent engine 524 may determine thathotel is a domain that is close to the restaurant domain and thus, a newintent “making hotel reservations” (in the form of {action=makingreservations; domain=hotel}) likely makes sense to the person 102 aswell. The new intent “making hotel reservations,” which is notdetermined from user data directly, may be added to the personal intentspace in the intent database 534 by the intent engine 524. In someembodiments, the intent database 534 include a common intent space forthe general population. Some intents that are not in the personal intentspace may exist in the common intent space. If they are popular amongthe general population or among people similar to the person 102, thenthe intent engine 524 may consider those intents as candidates as wellin intent estimation.

In estimating intent of the person 102, the intent engine 524 receivesthe input from the user interface 502 or any information retrieved bythe person-centric knowledge retriever 526 and tries to identify anyaction and/or domain from the input that is also in the intent spaces inthe intent database 534. If both action and domain can be identifiedfrom the input, then an intent can be derived directly from the intentspace. Otherwise, the intent engine 524 may need to take the contextualinformation from the contextual information identifier 512 to filterand/or rank the intent candidates identified from the intent space basedon the action or domain. In one example, if the input involves only theaction “making reservations” without specifying the domain, the intentengine 524 may first identify a list of possible domains that can becombined with such action according to the personal intent space, suchas “hotel” and “restaurant.” By further identifying that the locationwhere the input is made is at a hotel, the intent engine 524 mayestimate that the person 102 likely intends to make restaurantreservations as he is already in the hotel. It is understood that insome cases, neither action nor domain can be identified from the inputor the identified action or domain does not exist in the intent space,the intent engine 524 may estimate the intent purely based on theavailable contextual information. Various components in theperson-centric INDEX system 202, including the search engine 516, thesuggestion engine 504, the dynamic card builder 528, and theperson-centric knowledge retriever 526, may take advantage of the intentestimated by the intent engine 524.

The search engine 516 in this embodiment receives a search query fromthe query interface 506 and performs a general web search or a verticalsearch in the public space 108. Intent estimated by the intent engine524 for the search query may be provided to the search engine 516 forpurposes such as query disambiguation and search results filtering andranking In some embodiments, some or all of the search results may bereturned to the query interface 506 in their native format (e.g.,hyperlinks) so that they can be presented to the person 102 on aconventional search results page. In this embodiment, some or all of thesearch results are fed into the dynamic card builder 528 for building adynamic search results card based on the estimated intent. For instance,if the intent of the query “make reservation” is estimated as “makingrestaurant reservations,” then the top search result of a localrestaurant may be provided to the dynamic card builder 528 for buildinga search results card with the name, directions, menu, phone number, andreviews of the restaurant.

The Q/A engine 518 in this embodiment receives a question from the Q/Ainterface 508 and classifies the question into either a personal ornon-personal question. The classification may be done based on a modelsuch as a machine learning algorithm. In this embodiment, the Q/A engine518 may check the person-centric knowledge database 532 and/or theprivate database 548 and semi-private database 546 in the person-centricspace 200 via the person-centric knowledge retriever 526 to see if thequestion is related to any private, semi-private data, or personalknowledge of the person 102. For instance, the question “who is TaylorSwift” is normally classified as a non-personal question. But in thecase if “Taylor Swift” is in the person 102's Contacts or social mediafriend list, or if “Taylor Swift” has sent emails to the person 102, theQ/A engine 518 then may classify the question as a personal question.For non-personal questions, any known approaches may be used to obtainthe answers.

Once the question is classified as personal, various features includingentities and relationships are extracted by the Q/A engine 518 from thequestion using, for example, a machine learned sequence tagger. Theextracted entities and relationships are used to traverse, by theperson-centric knowledge retriever 526, the person-centric knowledgedatabase 532, which stores person-centric relationships stored in apre-defined form. In some embodiments, the person-centric relationshipsmay be stored in a triple format including one or more entities and arelationship therebetween. When the Q/A engine 518 finds an exact matchof relationship and entity, it returns an answer. When there is no exactmatch, the Q/A engine 518 takes into consideration a similarity betweenthe question and answer triples and uses the similarity to find thecandidate answers. To measure the similarity, words embedded over alarge corpus of user texts may be collected and trained by the Q/Aengine 518. The well-organized, person-centric information stored in theperson-centric space 200 and the person-centric knowledge database 532makes it possible for the Q/A engine 518 to answer a personal questionin a synthetic manner without the need of fully understanding thequestion itself. The answers generated by the Q/A engine 518 may beprovided to the dynamic card builder 528 for building answer cards.

The task generation engine 520 and the task completion engine 522 worktogether in this embodiment to achieve automatic task generation andcompletion functions of the person-centric INDEX system 202. The taskgeneration engine 520 may automatically generate a task in response to avariety of triggers, including for example, a task request from theperson 120 received via the task interface 510, an answer generated bythe Q/A engine 518, a card constructed by the dynamic card builder 528,or an event or behavior pattern related to the person 102 from theperson-centric space 200 and/or the person-centric knowledge database532. Intent may have also been taken into account in some embodiments intask generation. The task generation engine 520 in this embodiment alsodivides each task into a series of task actions, each of which can bescheduled for execution by the task completion engine 522. The tasktemplate database 538 stores templates of tasks in response to differenttriggers. The task generation engine 520 may also access the tasktemplate database 538 to retrieve relevant templates in task generationand update the templates as needed. In some embodiments, the taskgeneration engine 520 may call the dynamic card builder 528 to build acard related to one or more tasks so that the person 102 can check andmodify the automatically generated task as desired.

The tasks and task actions are stored into task lists 540 by the taskgeneration engine 520. Each task may be associated with parameters, suchas conditions in which the task is to be executed and completed. Eachindividual task action of a task may also be associated with executionand completion conditions. The task completion engine 522 fetches eachtask from the task lists 540 and executes it according to the parameterassociated therewith. For a task, the task completion engine 522dispatches each of its task actions to an appropriate executor toexecute it, either internally through the person-centric knowledgeretriever 526 or externally in the public space 108, semi-private space106, or private space 104. In one example, task actions such as “findingavailable time on Tuesday for lunch with mom” can be completed byretrieving calendar information from the private database 548 in theperson-centric space 200. In another example, task actions like“ordering flowers from Aunt Mary's flower shop” can only be completed byreaching out to the flower shop in the public space 108. The taskcompletion engine 522 may also schedule the execution of each taskaction by putting it into a queue. Once certain conditions associatedwith a task action are met, the assigned executor will start to executeit and report the status. The task completion engine 522 may update thetask lists 540 based on the status of each task or task action, forexample, by removing completed tasks from the task lists 540. The taskcompletion engine 522 may also provide the status updates to theperson-centric knowledge retriever 526 such that the status updates ofany ongoing task become available for any component in theperson-centric INDEX system 202 as needed. For instance, the dynamiccard builder 528 may build a notice card notifying the person that yourtask request “sending flowers to mom on Mother's day” has beencompleted.

As a component that supports intent-based dynamic card construction forvarious front-end components, the dynamic card builder 528 receivesrequests from the search engine 516, the Q/A engine 518, the taskgeneration engine 520, or the person-centric knowledge retriever 526. Inresponse, the dynamic card builder 528 asks for the estimated intentassociated with the request from the intent engine 524. Based on therequest and the estimated intent, the dynamic card builder 528 cancreate a card on-the-fly by selecting suitable card layout and/ormodules from the card module database 536. The selection of modules andlayouts is not predetermined, but may depend on the request, the intent,the context, and information from the person-centric space 200 and theperson-centric knowledge database 532. Even for the same queryrepeatedly received from the same person 102, completely different cardsmay be built by the dynamic card builder 528 based on the differentestimated intents in different contexts. A card may be created bypopulating information, such as search results, answers, status updates,or any person-centric information, into the dynamically selected andorganized modules. The filling of information into the modules on a cardmay be done in a centralized manner by the dynamic card builder 528regardless of the type of the card or may be done at each componentwhere the request is sent. For example, the Q/A engine 518 may receivean answer card construction with dynamically selected and organizedmodules on it and fill in direct and indirect answers into those modulesby itself.

In one embodiment, the person-centric knowledge retriever 526 can searchthe person-centric space 200 and the person-centric knowledge database532 for relevant information in response to a search request from theintent engine 524, the query interface, the Q/A engine 518, thesuggestion engine 504, the dynamic card builder 528, or the taskgeneration engine 520. The person-centric knowledge retriever 526 mayidentify one or more entities from the search request and search for thematched entities in the person-centric knowledge database 532. Asentities stored in the person-centric knowledge database 532 areconnected by relationships, additional entities and relationshipsassociated with the matched entities can be returned as part of theretrieved information as well. As for searching in the person-centricspace 200, in one embodiment, the person-centric knowledge retriever 526may first look for private data in the private database 548 matching theentities in the search request. As data in the person-centric space 200are cross-linked by cross-linking keys, the entities and/or thecross-linking keys associated with the relevant private data may be usedfor retrieving additional information from the semi-private database 546and the public database 544. For instance, to handle a search requestrelated to “amazon package,” the person-centric knowledge retriever 526may first look for information in the private database 548 that isrelevant to “amazon package.” If an order confirmation email is found inthe private database 548, the person-centric knowledge retriever 526 mayfurther identify that the order confirmation email is associated with across-linking key “tracking number” in the package shipping domain.Based on the tracking number, the person-centric knowledge retriever 526then can search for any information that is also associated with thesame tracking number in the person-centric space 200, such as thepackage delivery status information from www.FedEx.com in the publicdatabase 544. As a result, the person-centric knowledge retriever 526may return both the order confirmation email and the package deliverystatus information as a response to the search request.

In some embodiments, the person-centric knowledge retriever 526 mayretrieve relevant information from multiple data sources in parallel andthen blend and rank all the retrieved information as a response to thesearch request. It is understood that information retrieved from eachsource may be associated with features that are unique for the specificsource, such as the feature “the number of recipients that are cc′d” inthe email source. In order to be able to blend and rank results fromdifferent sources, the person-centric knowledge retriever 526 maynormalize the features of each result and map them into the same scalefor comparison.

The cross-linking engine 542 in this embodiment associates informationrelevant to the person 102 from the private space 104, the semi-privatespace 106, and the public space 108 by cross-linking data based oncross-linking keys. The cross-linking engine 542 may first process allinformation in the private space 104 and identify cross-linking keysfrom the private space 104. For each piece of content in the privatespace 104, the cross-linking engine 542 may identify entities anddetermine the domain to which the content belongs. Based on the domain,one or more entities may be selected as cross-linking keys for thispiece of content. In one example, tracking number may be a cross-linkingkey in the package shipping domain. In another example, flight number,departure city, and departure date may be cross-linking keys in theflight domain. Once one or more cross-linking keys are identified foreach piece of information in the private space 104, the cross-linkingengine 542 then goes to the semi-private space 106 and the public space108 to fetch information related to the cross-linking keys. For example,the tracking number may be used to retrieve package delivery statusinformation from www.FedEx.com in the public space 108, and the flightnumber, departure city, and departure date may be used to retrieveflight status from www.UA.com in the public space 108. Informationretrieved by the cross-linking engine 542 from the private space 104,semi-private space 106, and public space 108 may be stored in theprivate database 548, semi-private database 546, and public database 544in the person-centric space 200, respectively. As each piece ofinformation in the person-centric space 200 is associated with one ormore cross-linking keys, they are cross-linked with other informationassociated with the same cross-linking keys, regardless which space itcomes from. Moreover, as the cross-linking keys are identified based onthe person's private data (e.g., emails), all the cross-linkedinformation in the person-centric space 200 are relevant to the person102.

Although only one database is shown in FIG. 5 for information from theprivate space 104, the semi-private space 106, or the public space 108,it is understood that information within a particular space may beorganized and stored in different databases in the person-centric space200. For instance, private data from emails, Contacts, calendars, andphotos may be stored in separate databases within the private database548; semi-private data from Facebook, Twitter, LinkedIn, etc. may bestored in separate databases within the semi-private database 546 aswell. Such arrangement may enable applying different feature extractionmodels to different data sources, which may be helpful for thesuggestion engine 504 and the person-centric knowledge retriever 526. Asmentioned above, the cross-linking engine 542 continuously anddynamically maintains and updates the person-centric space 200 on aregular basis and/or in response to any triggering event. For example,any internal operation, such as query search, question answering, ortask completion, may trigger the cross-linking engine 542 to update theaffected data or add missing data in the person-centric space 200.

The knowledge engine 530 in this embodiment processes and analyzes theinformation in the person-centric space 200 to derive analytic resultsin order to better understand the person-centric space 200. In oneembodiment, the knowledge engine 530 extracts entities from content inthe person-centric space 200 and resolves them to what they refer to(i.e., can disambiguate an extracted entity when it may refer tomultiple individuals). In addition to determining an entity type for anextracted entity name, the knowledge engine 530 may also determine aspecific individual referred to by this entity name. The knowledgeengine 530 can make use of contextual information and/or textualmetadata associated with the entity name in the email to disambiguatesuch cases, providing a high precision resolution.

The knowledge engine 530 also builds a person-centric knowledgerepresentation for a person 102 by extracting and associating data aboutthe person 102 from personal data sources. The person-centric knowledgerepresentation for the person 102 is stored in the person-centricknowledge database 532. The knowledge engine 530 can extract entitiesrelated to the person 102 and infer relationships between the entitieswithout the person 102's explicit declaration, and create, for example,a person-centric knowledge graph for the person 102 based on theentities and relationships. The knowledge elements that can be inferredor deduced may include, for example, the person 102's social contacts,and the person 102's relationships with places, events, or other users.

FIG. 6 is a flowchart of an exemplary process for building aperson-centric space, according to an embodiment of the presentteaching. Starting at 602, data from the private space 104 is obtained.The data includes any content that is private to a person, such asemails, Contacts, calendar events, photos, bookmarks, instant messages,usage records, and so on. Contextual information is obtained at 604. Thecontextual information includes, but is not limited to, user informationsuch as demographic information and interests and preferences, localeinformation, temporal information, device information, and user-sessioninformation (e.g., other user inputs in the same or adjacentuser-sessions). At 606, information from the private space data isextracted. The information may be cross-linking keys determined fromentities extracted from the private space data based on the domain ofthe private space data and/or the obtained contextual information.Person-centric data is then retrieved from the semi-private space at608. Similarly, person-centric data is retrieved from the public spaceat 610. In this embodiment, the person-centric data is retrieved basedon the cross-linking keys. At 612, all pieces of person-centric dataretrieved from the private space, semi-private space, and public spaceare cross-linked together to generate a person-centric space. In thisembodiment, the cross-linking is done based on the same cross-linkingkeys associated with these pieces of person-centric data. At 614,analytic data is derived from the person-centric space. For example,entities may be extracted from the person-centric space and aredisambiguated by the knowledge engine 530 to ascertain their extractmeanings Relationships between the entities may be inferred based oninformation from the person-centric space by the knowledge engine 530 aswell. Based on the entities and relationships, person-centric knowledgecan be derived and stored in the person-centric knowledge database 532.

FIG. 7 is a flowchart of an exemplary process for applying aperson-centric space for digital personal assistance, according to anembodiment of the present teaching. Starting at 702, an input from aperson is received. As the person enters the input, a preliminary intentis estimated and continuously updated at 704. The estimation may bebased on the current input and any contextual information currentlyavailable. At 706, one or more suggestions are generated based on thecurrent input and the estimated intent and provided to the person toassist completing the current input. A response to the suggestions isreceived from the person at 708. The response may be a selection of onesuggestion or ignoring the suggestions and finishing the input as theperson desires. Once the completed input is received, either as aselection of a suggestion or a fully-entered input, at 710, the intentis estimated again for the completed input. The intent may be estimatedbased on the completed input and the currently available contextualinformation. In some embodiments, if no input is received (e.g., whenthe person just logs into and has not entered anything yet), the intentmay be estimated based on the contextual information alone. At 712,person-centric knowledge is retrieved based on the input. In someembodiments, the estimated intent may be used for retrieving theperson-centric knowledge as well. As described above in detail, theinput may be a question, a task request, or a query. In any event,entities and/or relationships may be derived from the input and are usedfor retrieving relevant person-centric knowledge from the person-centricknowledge database 532. In some embodiments, additional information maybe retrieved from the person-centric space. Intent-based cards are builtat 714. Each card may be constructed based on a layout and one or moremodules that are selected based on the type of the card and theestimated intent. Content in each module may be filled in based on theperson-centric knowledge and any additional information retrieved at712. Optionally or additionally, at 718, the construction of a card maycause a task to be generated based on the estimated intent. Forinstance, an email card summarizing an online order confirmation emailmay trigger the generation of a task for automatically tracking thepackage delivery status. At 720, the task is executed. Nevertheless, at716, the intent-based cards, either an email card, an answer card, asearch results card, or a notice card, are provided to the person as aresponse to the input.

FIG. 8 depicts an exemplary scheme of building a person-centric spacefor each individual person via the person-centric INDEX system andapplications thereof, according to an embodiment of the presentteaching. In this embodiment, each person 102-1, . . . 102-n may accessits own person-centric INDEX system 202-1, . . . 202-n, respectively.The person-centric INDEX system 202 may be a stand-alone systeminstalled on each person 102-1, . . . 102-n's device, a cloud-basedsystem shared by different persons 102-1, . . . 102-n, or a hybridsystem in which some components are installed on each person 102-1, . .. 102-n's device and some components are in the cloud and shared bydifferent persons 102-1, . . . 102-n.

In this embodiment, individual person-centric spaces 200-1, . . . 200-nare generated for each person 102-1, . . . 102-n via its ownperson-centric INDEX system 202-1, . . . 202-n, respectively Forexample, person-centric space 1 200-1 includes the projections fromdifferent spaces related to person 1 102-1 from the perspectives ofperson 1 102-1 (e.g., the entire private space 1 104-1, parts of thesemi-private spaces 1-k 106-1, . . . 106-k that are relevant to person 1102-1, and a slice of the public space 108 that is relevant to person 1102-1). Each person 102-1, . . . 102-n then uses its own person-centricINDEX system 202-1, . . . 202-n to access its own person-centric space200-1, . . . 200-n, respectively. Based on inputs from a person to itsperson-centric INDEX system, outputs are returned based on informationfrom the person-centric space in any forms and styles, including, forexample, any conventional outputs such as search result pages with “bluelinks,” and any types of intent-based cards such as search resultscards, answer cards, email cars, notice cards, and so on.

FIG. 9 depicts an exemplary scheme in which a variety of dynamic cardsare built and provided to a person based on different intents estimatedfor the same query in different contexts, according to an embodiment ofthe present teaching. Conventionally, a static card that has beenpre-constructed for certain popular entities may be presented to aperson when the query from the person happens to include one of thosepopular entities. In contrast, intent-based cards according to thepresent teaching can be dynamically generated on-the-fly by theperson-centric INDEX system 202 responsive to a query 902 from theperson 102. In this example, the person 102 inputs the same query 902“super bowl” at different times. When the query 902 is entered threeweeks before the super bowl game, its temporal context 904 will likelycause the intent 906 to be estimated as “buying super bowl tickets.”Based on such intent, a card 908 is dynamically generated for buyingsuper bowl tickets, including information such as super bowl ticketprices, tips on how to purchase, purchase website, etc. In someembodiments, the generation of this card 908 would cause a task ofpurchasing super bowl tickets to be automatically generated andcompleted. As time passes, when the temporal context 910 changes to thesuper bowl night, when the person 102 enters the same query 902, theintent 912 will likely change to “watching super bowl game.”Accordingly, a different card 914 for online super bowl game streamingis built and presented to the person 102, which may include, forexample, websites currently streaming the game. When the game finishesand the temporal context 916 changes to the day after the super bowlgame, if the person 102 again enters the query 902, the intent 918 willlikely become “reading super bowl game reviews.” A card 920 of superbowl game reviews is constructed and presented to the person 102. It isunderstood that the examples described above are for illustrativepurpose and are not intended to be limiting.

FIG. 10 illustrates an exemplary answer card, according to an embodimentof the present teaching. The answer card 1000 in this example isdynamically constructed on-the-fly in response to the question “when ismy son's soccer game?” Based on the type of the card (answer card) andintent (finding out my son's soccer game date/time), the layout andmodules are determined as shown in FIG. 10. It is understood that theshape, size, and layout of the answer card 1000 is for illustrativepurpose only and may vary in other examples. In some embodiments, theshape, size, and layout may be dynamically adjusted to fit thespecification of the user device (e.g., screen size, display resolution,etc.).

In this example, the answer card includes an answer header module 1002indicating that the topic of the answer card 1000 is “Daniel's (my son'sname identified according to person-centric knowledge) Next SoccerGame.” The direct answer to the question is found from a private emailand provided in the date/time module 1004. Optionally, certain actionsrelated to the answer may be provided as well, such as “add to mycalendar” and “open related emails.” Other information related to thedirect answer is provided in other modules as well. The location module1006 provides the location, address, and map of the soccer game.Information such as location and address may be retrieved from the emailrelated to the game in the private database 548 of the person-centricspace 200, while the map may be retrieved from Google Maps in the publicspace 108. The weather module 1008 provides the weather forecast of thegame day, which may be retrieved from wwww.Weather.com in the publicspace 108. The contact module 1010 shows persons involved in the gameand their contact information retrieved from the email about the gameand private Contacts in the private database 548 of the person-centricspace 200. Optionally, action buttons may be provided to call thepersons directly from the answer card 1000. It is understood that theexample described above is for illustrative purpose and are not intendedto be limiting.

FIG. 11 illustrates an exemplary search result card, according to anembodiment of the present teaching. The search results card 1100 in thisexample is dynamically constructed on-the-fly in response to the query“amy adams.” Based on the type of the card (a search results card) andintent (learning more about actor Amy Adams), the layout and modules aredetermined as shown in FIG. 11. It is understood that the shape, size,and layout of the search results card 1100 is for illustrative purposeonly and may vary in other examples. In some embodiments, the shape,size, and layout may be dynamically adjusted to fit the specification ofthe user device (e.g., screen size, display resolution, etc.). In thisexample, the search results card 1100 includes a header module 1102 withthe name, occupation, and portrait of Amy Adams. The bio module 1104includes her bio retrieved from Wikipedia, and the movies module 1106includes her recent movies. In the movies module 1106, each movie may bepresented in a “mini card” with the movie's name, release year, poster,and brief instruction, which are retrieved from www.IMDB.com. The moviesmodule 1106 is actionable so that a person can swap the “mini cards” tosee information of more her movies. If more modules cannot be shownsimultaneously due to the size of the search results card 1100 (forexample when it is shown on a smart phone screen), tabs (e.g., “Latest,”“About”) may be used to display different modules. It is understood thatthe example described above is for illustrative purpose and are notintended to be limiting.

FIG. 12 depicts an exemplary scheme of automatic online order emailsummary and package tracking via cross-linked data in a person-centricspace, according to an embodiment of the present teaching. Variousaspects of the present teaching are illustrated in FIG. 12 as well asrelated FIGS. 13-15, including cross-linking data from different spaces,entity extraction and building person-centric knowledge representation,dynamic card productions based on intent, answering personal questions,and automatic task generation and completion. In this example, at timet0, an order confirmation email 1202 is received from www.Amazon.com.The email 1202 in the private space is processed to extract and identifyentities. The entities include, for example,seller/vendor—www.Amazon.com, recipient/person—Mike, order date—Dec. 25,2015, item—Contract Case book, shipping carrier—FedEx, trackingnumber—12345678, and estimated delivery date: Jan. 1, 2016. In responseto receiving the email 1202, an email card 1204 summarizing the email1202 is generated and may be provided to Mike automatically or upon hisrequest.

The generation of the email card 1204 in this example automaticallyinitiates the generation of task 1 1206 for checking package deliverystatus. The details of task 1 1206 will be described in FIG. 13. Inorder to check the package delivery status, one or more cross-linkingkeys in the package shipping domain are identified among the entitiesextracted from the email 1202. As shown in FIG. 13, the entity “shippingcarrier—FedEx” is a cross-linking key used for identifying the websiteof FedEx 1208 in the public space, and the entity “trackingnumber—12345678” is a cross-linking key used for calling the statuscheck API 1210 of FedEx 1208. Based on the tracking number, packagedelivery status information 1212 is retrieved from FedEx 1208. Differentpieces of information from the private space and public space are thuscross-linked based on the cross-linking keys and can be projected intothe person-centric space.

At time t1, in response to an input from Mike (e.g., a question “whereis my amazon order?”), an answer card 1214 is dynamically generatedbased on private information in the email card 1204 and the publicpackage delivery status information 1212. The answer card 1214 ispresented to Mike as an answer to his question. In this example, thegeneration of the answer card 1214 automatically initiates another task2 1216 for monitoring and reporting package delivery status update.According to task 2 1216, package delivery status information 1212 maybe regularly refreshed and updated according to a schedule (e.g., everytwo hours) or may be dynamically refreshed and updated upon detectingany event that affects the package delivery. In this example, at timest2 and tn, certain events, such as package being delayed due to severeweather or package being delivered, trigger the generation of noticecards 1218, 1220, respectively. It is understood that the exampledescribed above is for illustrative purpose and are not intended to belimiting.

FIG. 13 illustrates an exemplary task with a list of task actions forautomatic package tracking Task 1 1206 for tracking package deliverystatus in this example includes a series of task actions (task actionlist): identifying shipping carrier 1302, identifying tracking number1304, obtaining shipping carrier's URL 1306, calling shopping carrier'sstatus check API using the tracking number 1308, extracting statusinformation 1310, and filling in the card 1312. Each task action may beassociated with parameters such as conditions in which the task actionis to be executed. For example, for task action 1312 “filling in thecard,” the condition may be filling the current package delivery statusinto an answer card when a question about the package delivery status isasked by the person or filling the current package delivery status intoa notice card of package delivery status update without waiting for anyinput from the person. Some task actions (e.g., 1302, 1304) may beexecuted by retrieving relevant information from the person-centricspace 200 and/or the person-centric knowledge database 532, while sometask actions (e.g., 1308) need to be completed in the public space 108.It is understood that the example described above is for illustrativepurpose and are not intended to be limiting.

FIG. 14 illustrates a series of exemplary cards provided to a person inthe process of automatic online order email summary and package trackingIn this example, the email card 1204 is automatically generatedresponsive to receiving the amazon order confirmation email 1202 andsummarizes the email 1202 based on the entities extracted from the email1202 and relationships thereof. The email card 1204 includes a headermodule “My Amazon Oder” and an order module with entities of item andprice. A “buy it again” action button may be added in the order module.The email card 1204 also includes a shipping module with entities ofshipping carrier, tracking number, and scheduled delivery date.

In this example, the answer card 1214 is generated in response to aquestion from the person about the status of the package. The answercard 1214 includes the header module and order module (but with lessinformation as the order information is not a direct answer to thequestion). The answer card 1214 includes a shipping module with richinformation related to shipping, which is retrieved from both theprivate email 1202 and FedEx 1208. The information includes, forexample, entities of shipping carrier, tracking number, and scheduleddelivery date from the private email 1202, and current estimateddelivery date, status, and location from FedEx 1208.

In this example, multiple notice cards 1218, 1220 are automaticallygenerated in response to any event that affects the status of thepackage. Each notice card 1218, 1220 includes an additional notificationmodule. If any other information is affected or updated due to theevent, it may be highlighted as well to bring to the person's attention.In notice card 1 1218, shipment is delayed due to a winter storm in ABCtown and as a consequence, the current estimated delivery date ischanged according to information retrieved from FedEx 1208. According tonotice card N 1220, the package has been delivered to Mike's home. It isunderstood that the examples described above are for illustrativepurpose and are not intended to be limiting.

FIG. 15 illustrates exemplary entities extracted from a person-centricspace and their relationships established in the process of automaticonline order email summary and package tracking. As described above, theperson-centric knowledge database 532 stores person-centric knowledgeorganized in the form of entity-relationship-entity triples. Entitiesextracted from the amazon order confirmation email 1202 are formed intoentity-relationship-entity triples by the knowledge engine 530. In theexample of FIG. 15, entity “Mike” 1502 from the recipient field of theemail 1202 is determined as the person using the person-centric INDEXsystem 202, and entity “FedEx” 1504 is determined as a shipping carrierwith a short-term relationship 1506 with entity “Mike” 1502. Attributes1508 may be associated with the relationship 1506 including, forexample, temporal attribute, tracking number, shipping item, sender,etc. These attributes may include related entities extracted from theemail 1202 and any other attributes inferred based on the relationship1506. It is noted that the relationship 1506 between entity “Mike” 1502and entity “FedEx” 1504 is a short-term, temporary relationship in thesense that the relationship 1506 will become invalid after the shipmentis completed, as indicated by the temporal attribute. In this example,entity “Mike” 1502 and another entity “Amazon” 1510 establish along-term relationship 1512 with a different set of attributes 1514thereof. The attributes 1514 include, for example, the temporalattribute, item, item rating, and so on. The relationship 1512 islong-term in this example because Mike has been repeatedly ordered goodsfrom Amazon, which has become his behavior pattern or preference. It isunderstood that the examples described above are for illustrativepurpose and are not intended to be limiting.

More detailed disclosures of various aspects of the person-centric INDEXsystem 202 are covered in different U.S. patent applications, entitled“Method and system for associating data from different sources togenerate a person-centric space,” “Method and system for searching in aperson-centric space,” “Methods, systems and techniques for providingsearch query suggestions based on non-personal data and user personaldata according to availability of user personal data,” “Methods, systemsand techniques for personalized search query suggestions,” “Methods,systems and techniques for ranking personalized and generic search querysuggestions,” “Method and system for entity extraction anddisambiguation,” “Method and system for generating a knowledgerepresentation,” “Method and system for generating a card based onintent,” “Method and system for dynamically generating a card,” “Methodand system for updating an intent space and estimating intent based onan intent space,” “Method and system for classifying a question,”“Method and system for providing synthetic answers to a personalquestion,” “Method and system for automatically generating andcompleting a task,” “Method and system for online task exchange,”“Methods, systems and techniques for blending online content frommultiple disparate content sources including a personal content sourceor a semi-personal content source,” and “Methods, systems and techniquesfor ranking blended content retrieved from multiple disparate contentsources.” The present teaching is particularly directed to updating anintent space and estimating intent based on an intent space.

FIG. 16 illustrates an exemplary intent space, according to anembodiment of the present teaching. The intent space 1600 in thisembodiment has two dimensions: actions and domains of knowledge(domains). Each data point in the intent space 1600 represent an intentthat characterized in each of the two dimensions. Each intent mayinclude a feature in the action dimension and a feature in the domaindimension. For example, an intent “making hotel reservations” include afeature “making reservations” in the action dimension and a feature“hotel” in the domain dimension, while another intent “making restaurantreservations” include the same feature “making reservations” in theaction dimension but a different feature “restaurant” in the domaindimension; still another intent “canceling hotel reservations” include adifferent feature “canceling reservation” in the action dimension butthe same feature “hotel” in the domain dimension. As shown in FIG. 16,the intent space 1600 is defined by the action dimension with features1-N and the domain dimension with features 1-K. In this embodiment, eachdata point (intersection) in the intent space represents an intentdefined by the corresponding features in the action and domaindimensions. For example, the intent 1602 is defined by action feature X1606 and domain feature Y 1604.

In this embodiment, not all data points (intersections) in the intentspace 1600 are positively labeled. When the number of features in eachdimension expands, the possible combinations (i.e., data points in theintent space 1600) will become huge, which makes traversing the entireor event part of the intent space 1600 (e.g., all action features in thesame domain column or all domain features in the same action row) tobecome time consuming. On the other hands, a lot of possiblecombinations (i.e., data points in the intent space 1600) are rarelyused either by a person for whom a personal intent space is built oreven by the general population. And some combinations do not make sense,such as “making reservations” and “newspaper.” Thus, in this embodiment,the initial intents of the intent space 1600 may be determined by userdata. For example, the initial features of each dimension in the intentspace 1600 may be manually determined by editorial selection and/orderived from actual inputs from a specific person or the generalpopulation. The inputs may be, for example, search queries, questions,task requests, etc. Some inputs may only have features in one dimension.For example, a query “movie ticket” only has features in the domaindimension but not in the action dimension. Similar to the features ofeach dimension, the initial intents in the intent space 1600 may bemanually determined by editorial selection and/or derived from actualinputs from a specific person or the general population. For intentsfrom the actual user inputs, they can be either explicitly recited(e.g., “making hotel reservation”) or estimated, for example, based oncontextual information. In some embodiments, one or more criteria may beset to determine whether an intent can be included in the intent space1600 even it was from the actual user inputs. For instance, an intentmay need to repeatedly appear over a certain number of times in order tobe considered as a meaningful intent that can be added into the intentspace 1600.

On one hand, it may be undesirable to include all possible combinationsin the intent space 1600 as candidates for intent estimation; on theother hand, the intent space 1600 needs to be kept updating to have moremeaningful intents that can be used for intent estimation, which wouldhelp improve the availability and accuracy of intent estimation. Addingnew features of a dimension and new intents to the current intent space1600 may be done in various ways, for example, by deriving intents fromactual user inputs as described above. In this embodiment, some experts,who are selected from general populations or users of the person-centricINDEX system 202, may manually add new features in any of the dimensionsand/or new intents based on existing features of the dimensions. Forexample, an academic expert may find that current intent space 1600 doesnot have the intent “reviewing academic conference papers” eitherbecause the feature “reviewing papers” in the action domain and/or thefeature “academic conference” does not exist in the intent space 1600 orthe combination has not been positively labeled even though bothfeatures do exist. The academic expert then may add the intent“reviewing academic conference papers” to the intent space 1600. Ifeither feature related to the intent does not exist before, then theintent space 1600 will also be updated to include the missing feature(s)as the academic expert adds the new intent. In some embodiments, someexperts may have the privilege to update a common intent space, whichmay be applied to the general population or a group of people. In someembodiments, each person using the person-centric INDEX system 202 mayact as an “expert” to update her/his own personal intent space.

In this embodiment, another way to update the current intent space 1600is learning new intents from initial intents based on models. As will bedescribed below in detail, a model may be provided to each dimension fordiscovering new features in the dimension. The model may includefeatures in a dimension and relationships thereof. The relationship maybe represented in various forms, such as taxonomy or a graph. Thecloseness (distance) between different features may be obtained from themodel. In the example of FIG. 16, the initial intent 1602 is thecombination of feature Y 1604 in the domain dimension and feature X 1606in the action dimension. A model in the action dimension may indicatethat feature X′ 1608 is close to feature X 1606 and thus, a newcombination of feature Y 1604 in the domain dimension and feature X′1608 in the action dimension is likely a meaningful intent even thoughit may not be positively labeled as an initial intent. Accordingly, anew intent 1610 may be discovered and added to the intent space 1600.Similarly, another model in the domain dimension may indicate thatfeature Y′ 1612 is close to feature Y 1604 and thus, a new combinationof feature X 1606 in the action dimension and feature Y′ 1612 in thedomain dimension is likely a meaningful intent even though it may not bepositively labeled as an initial intent. Accordingly, a new intent 1614may be discovered and added to the intent space 1600. The intent space1600 is thus updated to include the new intents 1610 and 1614 that arediscovered based on the initial intent 1602 and the models.

In this embodiment, each intent in the intent space 1600 is associatedwith a set of attributes. The attributes may include speciesinformation, personal information, historical information, temporalattributes, locale attributes, etc. The species information may indicatethe more fine-grained feature of the intent in a dimension. For example,for an intent “purchasing wines,” it may have species informationindicating whether the feature wine in the domain dimension has a morefine-grained feature, such as red wines, white wines, etc., or even morefine-grained features, such as zinfandel, pinot noir, chardonnay,merlot, etc. The personal information may be any information related tothe person for whom the intent is estimated and/or the one or morepersons from whom the intent was derived. The historical information maybe the inputs from which the intent was derived and the frequency theintent appears. The temporal attributes and locale attributes may be thedate/time and geo-location in which the intent was added to the intentspace 1600 or in which the intent has been chosen as the estimatedintent. It is understood that any other suitable attributes may beassociated with an intent in the intent space 1600. The attributes maybe used for selecting one intent from possible intent candidates whenestimating an intent for an input by checking the contextual informationrelated to the input against the attributes to find the relevancies. Theattributes of an estimated intent may also be used by other componentsof the person-centric INDEX system 202 when the estimated intent isprovided for various applications as disclosed in the present teaching.

The intent space 1600 may be used for estimating an intent for any userinput. When an input from a person is received, either in the form of aquery, a question, or a task request, the input may be parsed, andfeatures of domain and/or action may be derived from the parsed input.For example, domain features may be derived from entities extracted fromthe input, and action features may be derived from verbs in the input.If both features from the domain and action dimension can be derivedfrom the input, then their combination may explicitly indicate theintent, such as “making hotel reservations.” In some embodiments, thecombination may be checked against the intent space 1600 to see if it ispositively labeled in the intent space 1600. If not, it is possible thatthe intent is not meaningful due to typos in the input or because theintent space 1600 just has not capture the intent yet. For the lattercase, the intent space 1600 may be updated to populate this intent.Similarly, if any feature from the input does not appear in the intentspace 1600, the intent space 1600 may be updated to expand thecorresponding dimension to include such feature.

In the case that only features from the domain dimension or actiondimension can be obtained from the input, all the intents populated inthe intent space 1600 that correspond to such feature may be firstreturned as candidates. Assuming that the input is “makingreservations,” only action feature X 1606 “making reservation” can beobtained. According to the intent space 1600 shown in FIG. 1, twopossible intents 1602 and 1614 are related to action feature X 1606“making reservation”. For example, if the domain feature Y 1604 anddomain feature Y′ 1612 are “hotel” and “restaurant,” respectively, thenthe two possible intents 1602 and 1614 are “making hotel reservations”and “making restaurant reservations,” respectively. In order to estimatethe most likely intent for the input “making reservations,” anycontextual information available for the person and/or the input may beused. For instance, if the person is currently in a hotel based on thelocale information, then it is more likely she/her intents to makerestaurant reservations rather than hotel reservations because theperson is already in the hotel. In the case that there is only oneintent populated in the intent space 1600 based on the features derivedfrom the input, the intent is returned as the estimated intent.

FIG. 17 depicts an exemplary scheme of combing personal intent spacesinto a common intent space, according to an embodiment of the presentteaching. In this embodiment, each person may have her/his own personalintent space 1 created and updated based on user data of the personand/or manually by the person. Personal intent spaces 1700-1, 1700-2, .. . , 1700-m may be combined to generate a common intent space 1702 forall those persons 1-M. In some embodiments, the common intent space 1702may not be generated from the personal intent spaces 1700-1, 1700-2, . .. , 1700-m, but instead created and updated based on user data from thegeneral populations and/or manually by experts as described above. Aperson-centric INDEX system 202 for a person may use the personal intentspace 1700, the common intent space 1702, or both for estimating intent.In one example, the personal intent space 1700 may be created andmaintained on the person's local device, and the common intent space1702 may be created and maintained on a remote server for all users. Inanother example, both the personal intent space 1700 and common intentspace 1702 may be created and maintained on a remote server. In the casethat there is not enough user data of a person to create the initialpersonal intent space 1700, for example, because the person just startsto use the person-centric INDEX 202, the common intent space 1702 may beused as the initial personal intent space 1700. Over the time, as moreuser data can be obtained from the person, the common intent space 1702may be updated to become a personal intent space 1700 that is customizedfor the person.

FIG. 18 depicts an exemplary system diagram of an intent engine 524including an intent estimator 1802 and an intent database builder 1804,according to an embodiment of the present teaching. The intent estimator1802 in this embodiment is configured to estimate an intent for an inputfrom a person using the person-centric INDEX system 202 based on anintent space in the intent database 534. The intent database builder1804 in this embodiment is configured to create and update one or moreintent spaces in the intent database 534.

The intent estimator 1802 in this embodiment includes an input parser1806, a semantic analyzer 1808, mapping units 1810, intent filter 1812,and an intent candidate fetcher 1814. The input parser 1806 receives anyinput from a person via the user interface 502, such as queries,questions, or task requests. Text of each input is then parsed intounits by the input parser 1806. The semantic analyzer 1808 may analyzethe parsed input text to extract entities and verbs (and associatedphrases). For example, for an input “make hotel reservations,” theentities may include “hotel” and the verbs include “make” (andassociated phrase “make reservation”). The mapping units 1810 in thisembodiment includes an action mapping unit 1810-1 for mapping verbs andassociated phrases into features in the action dimension of an intentspace and a domain mapping unit 1810-2 for mapping entities intofeatures in the domain dimension of the intent space. It is understoodthat a variety of verbs or phrases may be mapped to the same actionfeature in an intent space. For example, “make reservations,” and“book,” “making reservations” can all be mapped to the same actionfeature in an intent space. Similarly, different entities may be mappedto the same domain feature in an intent space depending on thegranularity of the domains in the intent space. For instance,“zinfandel,” “pinot noir,” “chardonnay,” and “merlot” may all be mappedto the same domain feature “wine.” A mapping model 1816 is used by themapping units 1810 for mapping, which can be manually built based oncommon knowledge and the configuration of the intent space and/ormachined learned using training data.

The intent candidate fetcher 1814 receives the mapped features from themapping units 1810. It is understood that sometimes, only actionfeatures or domain features, but not both, can be obtained from aninput. The intent candidate fetcher 1814 in some embodiments may alsoretrieve person-centric knowledge via the person-centric knowledgeretriever 526 based on the entities and/or verbs from the semanticanalyzer 1808. In some cases, the person-centric knowledge may behelpful for the intent candidate fetcher 1814 to determine intentcandidates or even find the single intent directly. As described above,the intent candidate fetcher 1814 fetches all possible intents populatedin the intent space stored in the intent database 534 based on theavailable features. If features from both domain and action dimensionsare available, the intent candidate fetcher 1814 may return thecombination directly as the estimated intent. If such combination is notpopulated as an intent in the intent space, the intent candidate fetcher1814 may determine whether such combination shall be added to the intentspace or ignored. The determination may be made based on commonknowledge (e.g., certain combinations of domain and action features donot make sense), person-centric knowledge (e.g., certain combinations ofdomain and action features are in conflict with person-centricknowledge), and so on. If more than one intent is returned based on theaction feature or domain feature, then the intent candidate fetcher 1814forward them as intent candidates to the intent filter.

In case there is more than one intent fetched from the intent database534 by the intent candidate fetcher 1814, the intent filter 1812 isresponsible for filtering the intent candidates based on contextualinformation related to the person and/or the input provided by thecontextual information identifier 512. The contextual information mayinclude user-related information such as the person's demographicinformation and declared and inferred interests and preferences,date/time when the input is made, location where the input is sent,information related to the person's device itself (e.g., the devicetype, brand, and specification), and user-session information such asone or more inputs immediately before the current input. In one example,if the intent candidates include “buying flowers” and “buying videogames” and the date is one day before Mother's Day, then “buyingflowers” is more likely to be the intent of the person. In anotherexample, for the same intent candidates, if it is found that the personhas searched several newly released video games before the currentinput, then “buying video games” is more likely to be the intent of theperson. In addition to contextual information, person-centric knowledgeretrieved by the person-centric knowledge retriever 526 may also be usedby the intent filter 1812 to select one of the intent candidates. If theintent candidates include “buying flowers” and “buying video games” andthe person send several emails to her/his mother about dinner onMother's Day, then buying flowers” is more likely to be the intent ofthe person.

The intent database builder 1804 in this embodiment includes an inputscluster 1818, identifiers 1822, an attribute recorder 1824, expandingunits 1826, an intent database updater 1830, and an expert teaching unit1832. The inputs cluster 1818 in this embodiment groups inputs from theperson that have the same intent using a clustering model 1820. Theinputs cluster 1818 may cluster the queries, words and phrases leadingto the same intent. During clustering, the inputs cluster may detect andcategorize all entities in the input when present using, for example, aconditional random field (CRF)++ sequence tagger, which identifies anumber of entity types. The detected entities and their types may beused as features individually as well as in combination with theremaining n-grams in the input. These features can be used by the inputscluster 1818 for generalizing huge vocabulary and variants of entitynames. In addition, the inputs cluster 1818 may use word embeddingstrained on large set of unlabeled inputs, which allow the inputs cluster1818 to find similar words such as “car,” “vehicle,” “automobile,” and“BMW.” In one example, the inputs cluster 1818 may learn latentDirichlet allocation (LDA) topic models from documents to discover wordsthat belong to different topic and later use this information for eachword to express which topic it belongs to. The output from the inputscluster 1818 may be sets of groupings leading to the same intent andmeaning. For example, the inputs “I want to make a reservation to Wynn,”“cheap hotel reservation,” “book Wynn” may be part of the same cluster.

The identifiers 1822 in this embodiment includes an action identifier1822-1 and a domain identifier 1822-2 configured to identify features inthe action and domain dimensions, respectively, for each cluster ofinputs. The action identifier 1822-1 may extract the most common verbsor associated phrases from the cluster as the action feature. Words orphrases with the same or similar meanings may be consolidated. Thedomain identifier 1822-2 may take the most popular entities in thecluster as the domain feature. In addition to usage frequency, recencyor any other factors may be taken into consideration by the identifiers1822 in identifying the action and domain features. The attributerecorder 1824 in this embodiment may collect any attributes as mentionedabove with respect to FIG. 16 from each cluster and associate them withthe corresponding intent so that they can be stored with each data pointin the intent space. Contextual information from the contextualinformation identifier 512 may be supplied to the attribute recorder1824 for collecting and recoding certain attributes, such as temporaland locale attributes.

The expanding units 1826 in this embodiment include an action expandingunit 1826-1 and a domain expanding unit 1826-2 configured to discovernew intents based on expanding features in the action dimension anddomain dimension, respectively. As mentioned above, expanding models1828 may be applied in each dimension for determining new features ofeach domain. Turning now to FIG. 23, it shows one example of expandingmodels 1828 in the domain dimension. In this example, the expandingmodel 1828 is a domain taxonomy that includes a plurality of domain ofknowledge and their relationships. The domain taxonomy may be manuallydefined based on common knowledge and/or machined learned from usertraining data. In this example, the domain taxonomy includes first leveldomain features, such as travel, auto, sports, business, and so on. Inthe travel domain feature, the second level domain features include, forexample, hotel, restaurant, flight, car rental, etc. Based on thecloseness (distance) between domain features in the domain taxonomy, anew intent may be determined based on an existing intent. For example,if one cluster of inputs have an intent of “making hotel reservations,”the domain feature of that intent is identified as “hotel” in the domaintaxonomy. In accordance with the domain taxonomy, additional domainfeatures such as “restaurant,” “flight,” and “car rental” are deemed tobe close to the “hotel” domain feature. The domain expanding unit 1826-2thus may add new intents “making restaurant reservations,” “makingflight reservations,” and “making car rental reservations” based on theidentified additional domain features.

As to features in the action dimension, another expanding model 1828 maybe applied to determine new features and thus add new intents. Turningnow to FIG. 24, it shows one example of expanding models 1828 in theaction dimension. In this example, the expanding model 1828 is an actiongraph that includes a plurality of actions and their relationships. Theaction graph may be manually defined based on common knowledge and/ormachined learned from user training data. In this example, the actionfeature of “make reservation” is related to other actions featuresincluding “make payment,” “find location,” “check status,” “cancelreservation,” etc. Based on the closeness (distance) between actionfeatures in the action graph, a new intent may be determined based on anexisting intent. For example, if one cluster of inputs have an intent of“making hotel reservations,” the action feature of that intent isidentified as “make reservation” in the action graph. In accordance withthe action graph, additional action features such as “make payment,”“find location,” “check status,” and “cancel reservation” are deemed tobe close to the “make reservation” action feature. The action expandingunit 1826-1 thus may add new intents “cancel hotel reservations,” “checkhotel reservation status,” “make payment for hotel reservations,” and“find hotel reservations location” based on the identified additionaldomain features.

Returning back to FIG. 18, additional expanding models 1828 may beapplied in some embodiments. In the “Wynn reservation” example describedabove, the phrase “make a reservation at Wynn” may be generalized afterclustering to “make a reservation at <hotel>”. When the domain featureis removed, another expanding model 1828 may find other similar phrasesthat have different domain feature at the same position, for instance“make a reservation at Cascal,” where “Cascal” is a restaurant. Now theintent databased builder 1804 learns automatically that “makereservations” is an action feature that can be attached also to<restaurants>.

The intent database updater 1830 in this embodiment continuouslyreceives inputs from the identifiers 1822 and the expanding units 1826to create and update the intent space in the intent database 534. Theinputs to the intent databased updater 1830 may be domain features,action features, and/or intents derived from inputs of the person (e.g.,as attached to each input cluster). The inputs to the intent databasedupdater 1830 may be newly discovered domain features, action features,and/or intents expanded from the known intents based on expanding models1828. The attributes for each intent collected by the attribute recorded1824 may also be updated by the intent database updater 1830 inconjunction with the attribute recorder 1824.

The expert teaching unit 1832 in this embodiment may update the intentspace in the intent database 534 by expert teaching. FIG. 25 depicts anexemplary scheme of teaching new intent by experts, according to anembodiment of the present teaching. A group of experts 2502 may beselected from all users 2504 of the person-centric INDEX system 202. Theexperts 2502 may be selected based on various criteria, such as theirexpertise in certain areas, their knowledge about the person-centricINDEX system 202, and their social influence and reputations, to name afew. Additional user interface element may be provided to the experts2502 via the user interface of the person-centric INDEX system 202 or ameans independent of the person-centric INDEX system 202 so that theexperts 2502 can interact with the expert teaching unit 1832 to add newdomain features, add new action features, and/or add new intents to thecommon intent space 1702. In some embodiments, an expert may be grantedprevailed to add new domain features, new action features, and/or newintent in certain areas according to her/his expertise. Anadministrative review may be applied to review and approve any newdomain features, new action features, and/or new intent before they canbe populated into the common intent space 1700. It is understood that,in some embodiments, the experts 2502 may update the common intent space1702 by removing certain domain features, action features, and/orintents that are deemed to be inappropriate or nonsense. In thisembodiment, the update of the common intent space 1702 may affect eachperson's personal intent space 1700. For example, some or all of the newdomain features, new action features, and/or new intent in the commonintent space 1702 may be added to a person's personal intent space 1700.

The system components described above are for illustrative purposes;however, the present teaching is not intended to be limiting and maycomprise and/or cooperate with other elements to update an intent spaceand estimate intent based on an intent space. It is understood thatalthough the present teaching related to updating an intent space andestimating intent based on an intent space is described herein in detailas part of the person-centric INDEX system 202, in some embodiments, thesystem and method disclosed in the present teaching for updating anintent space and estimating intent based on an intent space can beindependent from the person-centric INDEX system 202 or as a part ofanother system.

FIG. 19 is a flowchart of an exemplary process for an intent estimator,according to an embodiment of the present teaching. Starting at 1902, aninput from a person is received. The input may be in the form of aquery, a question, or a task request. The text of the input includesparagraphs, sentences, phrases, words, letters, numbers, and symbols.The input is parsed at 1904. The text of input may be parsed into units.At 1906, the parsed input is analyzed to determine features in thedimensions of an intent space. The features include action features anddomain features. For instance, entities extracted from the input textmay be mapped to domain features, and verbs and associated phrases maybe mapped to action features. Intent candidate(s) are fetched from theintent space based on determined features at 1908. At 1910, whetherthere is more than one intent candidate is checked. If only one intentis fetched, moving to 1914, the intent is returned as the estimatedintent. Otherwise, at 1912, the intent candidates are filtered based oncontextual information. Optionally or additionally, the intentcandidates may be filtered based on person-centric knowledge of theperson. The remaining intent candidate is returned at 1914 as theestimated intent.

FIG. 20 is a flowchart of another exemplary process for an intentestimator, according to an embodiment of the present teaching. Startingat 2002, an input from a person is received. The input is parsed intounits at 2004. At 2006, it is determined whether any unit can be mappedto an action feature or a domain feature. If it is determined that onlyan action feature is mapped from a unit (e.g., a verb) of the input, theprocess continues to 2008 where all intents with the action feature arefetched from the intent space. At 2012, the fetched intents are filteredbased on contextual information. In some embodiments, the filtering at2012 may be based on any person-centric knowledge of the person as well.At 2014, the intent is retuned as the estimated intent of the input. At2006, if only a domain feature is mapped from a unit (e.g., an entity)of the input, the process continues to 2010 where all intents with thedomain feature are fetched from the intent space. At 2012, the fetchedintents are filtered based on contextual information. In someembodiments, the filtering at 2012 may be based on any person-centricknowledge of the person as well. At 2014, the intent is returned as theestimated intent of the input. If, at 2006, both an action feature and adomain feature are mapped from units of the input, then moving to 2016,where an intent as the combination of the action feature and domainfeature is fetched from the intent space. That is, if the number offeatures determined from the input equals the number of dimensions inthe intent space (two features in two dimensions in this example), thenthe combination of the features is the estimated intent.

FIG. 21 is a flowchart of an exemplary process for an intent databasebuilder, according to an embodiment of the present teaching. Starting at2102, input data is received. The input data is clustered into multipleclusters at 2104. At 2106, an intent is identified for each cluster. At2108, the initial intent space is built based on all intents of theclusters. If the input data is specific to a person, the intent spacemay be a personal intent space for the person. If the input data isreceived from a general population, then the intent space may be acommon intent space for the general population. At 2110, new intents arediscovered based on the existing intents. The intent space is updated at2112 to incorporate the new intents.

FIG. 22 is a flowchart of another exemplary process for an intentdatabase builder, according to an embodiment of the present teaching.Starting at 2202, entities are extracted from each piece of input data.At 2204, the extracted entities are categorized. Topics of words aredetermined in the input data at 2206. The input data is clustered basedon the categorized entities and the topics of words at 2208. At 2210,the most common verbs among all inputs in each cluster are extracted asthe action features. At 2212, the most popular entities among all inputsin each cluster are extracted as the domain features. Intents aredetermined based on the action features and domain features for eachcluster at 2214. At 2216, additional action features are identifiedbased on an action graph. At 2218, additional domain features areidentified based on a domain taxonomy.

FIG. 26 depicts the architecture of a mobile device which can be used torealize a specialized system implementing the present teaching. In thisexample, the device on which a person interfaces and interacts with theperson-centric INDEX system 202 is a mobile device 2600, including, butis not limited to, a smart phone, a tablet, a music player, a hand-heldgaming console, a global positioning system (GPS) receiver, and awearable computing device (e.g., eyeglasses, wrist watch, etc.), or inany other forms. The mobile device 2600 in this example includes one ormore central processing units (CPUs) 2602, one or more graphicprocessing units (GPUs) 2604, a display 2606, a memory 2608, acommunication platform 2610, such as a wireless communication module,storage 2612, and one or more input/output (I/O) devices 2614. Any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 2600.As shown in FIG. 26, a mobile operating system 2616 (e.g., iOS, Android,Windows Phone, etc.), and one or more applications 2618 may be loadedinto the memory 2608 from the storage 2612 in order to be executed bythe CPU 2602. The applications 2618 may include the entire or a portionof the person-centric INDEX 202. User interactions with the userinterface 502 of the person-centric INDEX 202 may be achieved via theI/O devices 2614 and provided to any component of the person-centricINDEX 202 on one or more remote servers via the communication platform2610.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (e.g., the person-centric INDEX system 202 described with respectto FIGS. 2-25). The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to updating an intent space and estimatingintent based on an intent space as described herein. A computer withuser interface elements may be used to implement a personal computer(PC) or other type of work station or terminal device, although acomputer may also act as a server if appropriately programmed. It isbelieved that those skilled in the art are familiar with the structure,programming and general operation of such computer equipment, and as aresult the drawings should be self-explanatory.

FIG. 27 depicts the architecture of a computing device which can be usedto realize a specialized system implementing the present teaching. Sucha specialized system incorporating the present teaching has a functionalblock diagram illustration of a hardware platform which includes userinterface elements. The computer may be a general purpose computer or aspecial purpose computer. Both can be used to implement a specializedsystem for the present teaching. This computer 2700 may be used toimplement any component of the person-centric INDEX system 202, asdescribed herein. For example, the person-centric INDEX system 202 maybe implemented on a computer such as computer 2700, via its hardware,software program, firmware, or a combination thereof. Although only onesuch computer is shown for convenience, the computer functions relatingto updating an intent space and estimating intent based on an intentspace as described herein may be implemented in a distributed fashion ona number of similar platforms, to distribute the processing load.

The computer 2700, for example, includes COM ports 2702 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 2700 also includes a central processing unit (CPU) 2704, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 2706,program storage and data storage of different forms (e.g., disk 2708,read only memory (ROM) 2710, or random access memory (RAM) 2712), forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by the CPU 2704.The computer 2700 also includes an I/O component 2714, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 2716. The computer 2700 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of updating an intent space and estimatingintent based on an intent space and/or other processes, as outlinedabove, may be embodied in programming. Program aspects of the technologymay be thought of as “products” or “articles of manufacture” typicallyin the form of executable code and/or associated data that is carried onor embodied in a type of machine readable medium. Tangiblenon-transitory “storage” type media include any or all of the memory orother storage for the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide storage at any time for thesoftware programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma server or host computer into the hardware platform(s) of a computingenvironment or other system implementing a computing environment orsimilar functionalities in connection with updating an intent space andestimating intent based on an intent space. Thus, another type of mediathat may bear the software elements includes optical, electrical andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless links, optical links or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables, copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude, for example: a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, anyother optical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution (e.g., an installation on an existing server). Inaddition, the method and system of updating an intent space andestimating intent based on an intent space as disclosed herein may beimplemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

We claim:
 1. A method, implemented on a computing device having at leastone processor, storage, and a communication platform capable ofconnecting to a network for estimating intent based on an intent spacehaving a plurality of dimensions, the method comprising: receiving aninput from a person; receiving at least one model, each of whichcharacterizes each of the plurality of dimensions based on a set offeatures and obtains a closeness in relationship between each pair offeatures in the dimension by computing a distance between the pair offeatures mapped in a feature space; analyzing the input to extract oneor more features in at least one of the plurality of dimensions in theintent space; obtaining contextual information related to the input; anddetermining, based on at least one of the distance, the contextualinformation, and the extracted one or more features, an intent from theintent space which includes a plurality of intents, each intent beingadded to the intent space based on a criterion corresponding to afrequency of occurrence of the intent being satisfied, wherein thedetermining further comprises: determining whether a number of theextracted one or more features from the input equals to a number of theplurality of dimensions, identifying, in response to a result of thedetermining being positive, the intent in the intent space based on theextracted one or more features from the input, identifying, in responseto the result of the determining being negative, a plurality of intentcandidates from the intent space based on the at least one model and theextracted one or more features, and selecting the intent from theplurality of intent candidates based on the contextual information. 2.The method of claim 1, wherein each intent in the intent space isassociated with a set of attributes, which include at least one ofspecies information, personal information, historical information,temporal attributes, and locale attributes.
 3. The method of claim 1,wherein plurality of dimensions include an action dimension and a domaindimension.
 4. The method of claim 1, wherein the contextual informationrelated to the input corresponds to a geographical location of theperson.
 5. A non-transitory, machine-readable medium having informationrecorded thereon for estimating intent based on an intent space having aplurality of dimensions, wherein the information, when read by amachine, causes the machine to perform the steps of: receiving an inputfrom a person; receiving at least one model, each of which characterizeseach of the plurality of dimensions based on the set of features andobtains a closeness in relationship between each pair of features in thedimension by computing a distance between the pair of features mapped ina feature space; analyzing the input to extract one or more features inat least one of the plurality of dimensions in the intent space;obtaining contextual information related to the input; determining,based on at least one of the distance, the contextual information, andthe extracted one or more features, an intent from the intent spacewhich includes a plurality of intents, each intent being added to theintent space based on a criterion corresponding to a frequency ofoccurrence of the intent being satisfied, wherein the determiningfurther comprises: determining whether a number of the extracted one ormore features from the input equals to a number of the plurality ofdimensions, identifying, in response to a result of the determiningbeing positive, the intent in the intent space based on the extractedone or more features from the input, identifying, in response to theresult of the determining being negative, a plurality of intentcandidates from the intent space based on the at least one model and theextracted one or more features, and selecting the intent from theplurality of intent candidates based on the contextual information. 6.The medium of claim 5, wherein the intent space is created with respectto a general population.
 7. The medium of claim 5, wherein the pluralityof dimensions includes an action dimension and a domain dimension. 8.The medium of claim 5, wherein the at least one model includes: a domaintaxonomy; and an action graph.
 9. The medium of claim 8, wherein thedomain taxonomy includes a plurality of domain of knowledge andcloseness there between.
 10. The medium of claim 8, wherein the actiongraph includes a plurality of actions and relationships there between.11. The medium of claim 5, wherein each intent in the intent space isassociated with a set of attributes, which include at least one ofspecies information, personal information, historical information,temporal attributes, and locale attributes.